Smart Wearables in Pediatric Heart Health

Behere, Shashank; Janson, Christopher M.

doi:10.1016/j.jpeds.2022.08.009

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…Although advances in smartphone technology and wearable sensors have resulted in a surge of ambulatory assessments with adolescents and adults [16][17][18], current commercially available devices typically rely on one unit of analysis, such as self-reported behavior [18], physiological functioning [19][20][21], or audio recordings (EAR; [22] Language ENvironment Analysis [LENA], [23,24]), and almost none are feasible and/or validated for use with infants or young children. Indeed, the limited number of wearables that have been (a) used in the home (b) with infants and young children (c) across extended periods of time (e.g., daylong recordings) and (d) are validated is striking, particularly when compared with the proliferation of infant-wearable biosensors that have been designed for clinical use in hospital settings.…”

Section: Contribution Of the Littlebeats™ Platformmentioning

confidence: 99%

“…The LENA system, which permits daylong audio recordings collected with an infant-worn recording device and includes proprietary software that automates the word counts of both infants and adults in the home, is a notable exception [23][24][25][26]. Commercially available infant wearables designed for home use that monitor physiological signals tend to lack rigorous validation (see [20,27]). Further, with respect to cardiac monitoring specifically, the quality signal of ECG makes it the gold standard compared with more noisy sensor signals used in wearables (e.g., phonocardiogram, photoplethysmography [28]).…”

Section: Contribution Of the Littlebeats™ Platformmentioning

confidence: 99%

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Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations

Islam,

McElwain,

et al. 2024

Sensors

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Across five studies, we present the preliminary technical validation of an infant-wearable platform, LittleBeats™, that integrates electrocardiogram (ECG), inertial measurement unit (IMU), and audio sensors. Each sensor modality is validated against data from gold-standard equipment using established algorithms and laboratory tasks. Interbeat interval (IBI) data obtained from the LittleBeats™ ECG sensor indicate acceptable mean absolute percent error rates for both adults (Study 1, N = 16) and infants (Study 2, N = 5) across low- and high-challenge sessions and expected patterns of change in respiratory sinus arrythmia (RSA). For automated activity recognition (upright vs. walk vs. glide vs. squat) using accelerometer data from the LittleBeats™ IMU (Study 3, N = 12 adults), performance was good to excellent, with smartphone (industry standard) data outperforming LittleBeats™ by less than 4 percentage points. Speech emotion recognition (Study 4, N = 8 adults) applied to LittleBeats™ versus smartphone audio data indicated a comparable performance, with no significant difference in error rates. On an automatic speech recognition task (Study 5, N = 12 adults), the best performing algorithm yielded relatively low word error rates, although LittleBeats™ (4.16%) versus smartphone (2.73%) error rates were somewhat higher. Together, these validation studies indicate that LittleBeats™ sensors yield a data quality that is largely comparable to those obtained from gold-standard devices and established protocols used in prior research.

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Section: Contribution Of the Littlebeats™ Platformmentioning

confidence: 99%

Section: Contribution Of the Littlebeats™ Platformmentioning

confidence: 99%

Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations

Islam,

McElwain,

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Although advances in smart phone technology and wearable sensors have resulted in a surge of ambulatory assessments with adolescents and adults [16][17][18], current commercially-available devices typically rely on one unit of analysis, such as self-reported behavior [18], physiological functioning [19][20][21] or audio recordings (EAR; [22] Language ENvironment Analysis [LENA, [23], [24]] , and almost none are feasible and/or validated for use with infants or young children. Indeed, the limited number of wearables that have been (a) used in the home (b) with infants and young children (c) across extended periods of time (e.g., daylong recordings) and (d) are validated is striking, particularly when compared with the proliferation of infant wearable biosensors that have been designed for clinical use in hospital settings.…”

Section: Contribution Of the Littlebeats™ Platformmentioning

confidence: 99%

“…The LENA system, which permits daylong audio recordings collected with an infant-worn recording device and includes proprietary software that automates word counts of both infants and adults in the home, is a notable exception [23][24][25][26]. Commercially available infant wearables designed for home use that monitor physiological signals tend to lack rigorous validation (see [20], [27]). Further, with respect to cardiac monitoring specifically, the quality signal of ECG makes it the gold standard compared with more noisy sensor signals used in wearables (e.g., phonocardiogram, photoplethysmography [28]).…”

Section: Contribution Of the Littlebeats™ Platformmentioning

confidence: 99%

Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations

Islam,

McElwain,

et al. 2024

Preprint

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Background: The use of wearable devices has burgeoned over the past decade, including wearables for infants and young children. Typically, such devices assess a single modality, and few have undergone scientific validation. To address this gap, we developed an infant wearable platform, LittleBeats™, that integrates electrocardiogram (ECG), motion, and audio sensors on a single printed circuit board to permit daylong remote assessments of infants and caregivers in home environments. Objective: Complementing our prior reports focused on LittleBeats™ data collected among infants and children in the home context, our main objective here is to present a technical validation of each sensor modality against established laboratory protocols and gold-standard equipment. Materials and Methods: We conducted five studies, including assessments of (a) interbeat interval (IBI) data obtained from the LittleBeats™ ECG sensor versus the gold-standard BIOPAC among adults (Study 1, N=16) and infants (Study 2, N=5), (b) performance of automated activity recognition (upright vs. walk vs. glide vs. squat ) among adults using accelerometer data obtained from LittleBeats™ versus Google Pixel 1 smartphone (Study 3, N=12), and (c) performance of speech emotion recognition (SER; Study 4, N=8) and automatic speech recognition (ASR; Study 5, N =12) algorithms among adult samples using audio data from LittleBeats™ versus smartphone. Results: Results for IBI data obtained from the LittleBeats™ ECG sensor indicate acceptable mean absolute percent error (MAPE) rates for both the adults (MAPE = 5.29% to 5.97%; Study 1) and infants (MAPE =0.96% to 1.66%; Study 2) across low- and high-challenge sessions, as well as expected patterns of change in respiratory sinus arrythmia (RSA) across sessions. For activity recognition (Study 3), LittleBeats™ data showed good to excellent performance (Accuracy = 89%, F1-score=88%; Cohen’s kappa =.79), although the smartphone outperformed LittleBeats™ by less than 4%. Finally, performance on the SER applied to LittleBeats™ versus smartphone audio data (Study 4) indicated comparable performance, with a matched-pairs test indicating no significant difference in error rates (p=.26). On the ASR task (Study 5), the best performing algorithm yielded relatively low word error rates, although LittleBeats™ (4.16%) versus smartphone (2.73%) error rates are somewhat higher. Discussion: Taken together, results from these controlled laboratory studies conducted predominantly with adults indicate that the LittleBeats™ sensors yield data quality that is largely comparable to those obtained from gold-standard devices and protocols that have been used extensively in prior research. The advantages of the LittleBeats™ platform is the (a) integration of multiple sensors into one platform that was (b) designed specifically for use with infants and young children. Leveraging data from the multiple modalities and fine-tuning postprocessing steps to further increase data quality, combined with assessing data quality among infants and young children in their natural environments, will be key next steps in this research program.

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Resource Use and Clinical Outcomes in Infants with Supraventricular Tachycardia Monitored with the Owlet Smart Sock

Silverstein,

Dreger,

Anza

et al. 2024

The Journal of Pediatrics

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Smart Wearables in Pediatric Heart Health

Cited by 7 publications

References 33 publications

Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations

Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations

Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations

Resource Use and Clinical Outcomes in Infants with Supraventricular Tachycardia Monitored with the Owlet Smart Sock

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